code: def inverse_transform(self, X):
      if len(self.columns):
          if self.scale_method == self.SCALE_NORMAL:
              X.loc[:, self.columns] = self.scale.inverse_transform(
                  X[self.columns]
              )
          elif self.scale_method == self.SCALE_LOG_AND_NORMAL:
              X[self.columns] = self.scale.inverse_transform(
                  X[self.columns]
              )
              X[self.columns] = np.exp(X[self.columns])
  
              X.loc[:, self.columns] += self.X_min_values - 1
      return X
  
file_path: mljar-supervised/supervised/preprocessing/scale.py
imports: import numpy
  import pandas
  from sklearn import preprocessing
label_desc: Use image node_id 4 for calling the Scale obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X